Using Imaging Spectroscopy to study soil properties

Water erosion creates negative impacts on agricultural production, infrastructure, and water quality across the world. Regional-scale water erosion assessment is important, but limited by data availability and quality. Satellite remote sensing can contribute through providing spatial data to such assessments. During the past 30 years many studies have been published that did this to a greater or lesser extent. The objective of this paper is to review methodologies applied for water erosion assessment using satellite remote sensing. First, studies on erosion detection are treated. This comprises the detection of erosion features and eroded areas, as well as the assessment of off-site impacts such as sediment deposition and water quality of inland lakes. Second, the assessment of erosion controlling factors is evaluated. Four types of factors are discussed: topography, soil properties, vegetation cover, and management practices. Then, erosion mapping techniques are described that integrate products derived from satellite remote sensing with additional data sources. These techniques include erosion models and qualitative methods. Finally, validation methods used to assess the accuracy of maps produced with satellite data are discussed. It is concluded that a general lack of validation data is a main concern. Validation is of utmost importance to achieve regional operational monitoring systems, and close collaboration between the remote sensing community and field-based erosion scientists is therefore required.

Satellite remote sensing for water erosion assessment: A review

This article discusses research in which the authors applied the Revised Universal Soil Loss Equation (RUSLE), remote sensing, and geographical information system (GIS) to the maping of soil erosion risk in Brazilian Amazonia. Soil map and soil survey data were used to develop the soil erodibility factor (K), and a digital elevation model image was used to generate the topographic factor (LS). The cover-management factor (C) was developed based on vegetation, shade, and soil fraction images derived from spectral mixture analysis of a Landsat Enhanced Thematic Mapper Plus image. Assuming the same climatic conditions and no support practice in the study area, the rainfall–runoff erosivity (R) and the support practice (P) factors were not used. The majority of the study area has K values of less than 0·2, LS values of less than 2·5, and C values of less than 0·25. A soil erosion risk map with five classes (very low, low, medium, medium-high, and high) was produced based on the simplified RUSLE within the GIS environment, and was linked to land use and land cover (LULC) image to explore relationships between soil erosion risk and LULC distribution. The results indicate that most successional and mature forests are in very low and low erosion risk areas, while agroforestry and pasture are usually associated with medium to high risk areas. This research implies that remote sensing and GIS provide promising tools for evaluating and mapping soil erosion risk in Amazonia. Copyright © 2004 John Wiley & Sons, Ltd.

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Mapping soil erosion risk in Rondônia, Brazilian Amazonia: using RUSLE, remote sensing and GIS

Traditional soil analyses are expensive, time-consuming, and may also result in environmental pollutants. The objective of this study was to develop and evaluate a methodology to measure soil attributes using spectral reflectance (SR) as an alternative to traditional methods. Tropical Brazilian soils were sampled over a 196-ha area divided into grids. Samples (n 5 184) were obtained from the 0- to 20- and 80- to 100-cm depths and georeferenced. The laboratory SR data were obtained using a Spectroradiometer (400–2 500 nm). Satellite reflectance values were sampled from corrected Landsat Thematic Mapper (TM) images. Particle-size distribution and chemical analysis (organic matter [OM], cation-exchange capacity [CEC], total SiO2 ,F e 2O3, TiO2, sum of cations, cation, and Al saturation) were performed in the laboratory. Statistical analysis and multiple regression equations for soilattribute predictionsusingradiometricdataweredeveloped.Laboratory data used 22 bands and 13 ‘‘Reflectance Inflexion Differences, RID’’ from different wavelength intervals of the optical spectrum. However, the satellite data used only the reflectance of the 1, 2, 3, 4, 5, and 7 TM-Landsat bands. Multiple regression equations were derived from surface and subsurface soil layers. Estimations of some tropical soil attributes were possible using laboratory spectral analysis. Laboratory SR yielded high correlations with traditional laboratory analyses (R 2 . 0.79) for the soil attributes such as clay, sand, TiO2, and Fe2O3. Satellite spectral data correlated well with most of the soil attributes such as clay, Fe2O3, and TiO2 (reaching R 2 5 0.72). The use of soil analysis methodology by satellite and/or ground remote sensing constitutes an alternative to traditional routine laboratory analysis.

Spectral Reflectance Methodology in Comparison to Traditional Soil Analysis

The detection and identification of plant diseases is crucial for an appropriate and targeted application of plant protection measures in crop production. Recently, intensive research has been conducted to develop innovative and technology-based optical methods for plant disease detection. In contrast to common visual rating and detection methods, optical sensors are able to measure pathogen-induced changes in the plant physiology non-invasively and objectively. Several studies showed that especially hyperspectral sensors are valuable tools for disease detection, identification and quantification on different scales from the tissue to the canopy level. This review describes the basic principles of hyperspectral measurements and different types of available hyperspectral sensors. Possible applications of hyperspectral sensors on different scales for disease detection and plant protection are discussed and evaluated. The advantages and disadvantages on each particular scale, as well as the impact of external factors, such as: light, wind, viewing angle, for measurements in laboratories, greenhouses and fields, are critically assessed in order to support researchers and agriculture technicians. Additionally, a comprehensive literature review about the use of hyperspectral sensors on these different scales for plant disease detection reflects the possibilities of non-invasive measurement systems. This highlights advantages of hyperspectral sensors when investigating plant–pathogen interactions through multiple examples. By some approaches, detection before visible symptoms appear is feasible. The potential of hyperspectral sensors as a tool for disease identification and quantification, based on disease characteristic changes in the plants spectral signature, is discussed as well. The review is concluded with an overview on different data analysis methods, which are required to extract key information from gathered hyperspectral datasets.

Benefits of hyperspectral imaging for plant disease detection and plant protection: a technical perspective

Remote sensing data, a GIS, and the Universal Soil Loss Equation model (USLE) are used to develop maps of erosion risk in the Piracicaba River basin, southeastern Brazil. The mapping program was designed to determine soil erosion losses under various land uses and the extent to which land use changes affected erosion risk during 1978–1993. To evaluate the latter, the USLE model was used to simulate erosion risk during January, the highest-precipitation month, in 1978 and 1993. This made it possible to identify the areas of highest erosion risk and to develop soil/water conservation countermeasures.

Gis erosion risk assessment of the piracicaba river basin, southeastern brazil

The authors compare information on soil maps of the Sao Paulo region, Brazil (obtained by four different methodologies) with ground truth data and a preexisting semi-detailed soil map. The methodologies for compiling the new soil maps included: (1) conventional (field) cartographic methods; (2) air photo interpretation; (3) automated processing of radiometric data; and (4) use of radiometric data in conjunction with air photo analysis and planimetric/altimetric observations. The remote sensing methodologies produced soil maps similar to the detailed map (1) and substantially better than the pre-existing semi-detailed one. The utility of these techniques to soil surveys at rather large scales, and for tropical soil mapping in general, is assessed.

José Alexandre M. Demattě

Papers

Gis erosion risk assessment of the piracicaba river basin, southeastern brazil

Remote sensing in the recognition and mapping of tropical soils developed on topographic sequences